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Karnataka Soil

- Citation Author(s):
- Submitted by:
- Gauri Kalnoor
- Last updated:
- Fri, 01/10/2025 - 09:21
- DOI:
- 10.21227/nqjf-7784
- Data Format:
- License:
Abstract
With the increase in world population, agricultural planning is significant to ensure food security. Timely recommendations for crops could be valuable for planning food production and maintaining food sustainability. This proposed work suggests a crop recommendation model considering physical soil characteristics, chemical soil characteristics, climate, and crop characteristics, using Improved Deep Belief Networks (IDBN). For this study, four important Indian crops—rice, maize, finger millet and sugarcane were taken into account. The soil, weather, and crop datasets were gathered from different sources and subjected to feature selections method to extract optimal features. The crop recommendation model was developed using the proposed IDBN with Gaussian Restricted Boltzmann Machines and Ranger Optimizer with selected optimal features as input. Gaussian Restricted Boltzmann Machines enable the use of continuous values, and Ranger Optimizer ensures rapid convergence with selected optimal features as input. The findings from the investigation confirm that the suggested IDBN outperforms conventional Deep Belief Networks (DBN). The effectiveness of the crop recommendation model was evaluated using IDBN, and its performance was compared with several other machine learning algorithms.
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Comments
i need this dataset for my academics
Hi This is Dr. Randhir kumar from CSE department SRM University, AP. I need your datasets for academic project for my B.Tech. students. Kindly send me if poosible or reply me.
I am a student.Working on agriculture related project.Can you please provide this dataset
I am a student at Bennett University . Kindly provide me an access to this dataset so that i can perform well on my project related to agriculture.